Robust constrained predictive controllers for hot rolling mills: Disturbance uncertainty case

Abstract

Looper—tension control among various control problems in hot rolling mills (HRMs) has significance, in particular, because it affects both operational stability of the process and dimensional quality of the products. The control target in this control loop is to maintain looper angle and strip tension simultaneously at their desired values. The most difficult challenge in the controller design arises from the interaction, between strip tension and looper angle, and uncertainty coming from disturbances and a model mismatch. Recently, there has been some research to investigate the potential benefits of model predictive control (MPC) for this control loop. However, most of them used constrained optimization based on nominal models, and this often caused constraint violations for the uncertain case. The purpose of this paper is to develop robust MPC algorithms for the hot rolling mill process, in the presence of disturbance uncertainty, in order to improve the constraint handling performance.